Triple

T9601633
Position Surface form Disambiguated ID Type / Status
Subject Sergei Bubka E231862 entity
Predicate trainingBase P11445 FINISHED
Object Donetsk E110135 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Donetsk | Statement: [Sergei Bubka, trainingBase, Donetsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Donetsk
Context triple: [Sergei Bubka, trainingBase, Donetsk]
  • A. Donetsk chosen
    Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
  • B. Zaporizhzhia
    Zaporizhzhia is a major industrial city in southeastern Ukraine, known for its large hydroelectric power plant on the Dnieper River and its significant role in the country’s energy and manufacturing sectors.
  • C. Donetsk Oblast
    Donetsk Oblast is an industrial and heavily urbanized region in eastern Ukraine, historically known for coal mining and metallurgy and currently a focal point of the Russo-Ukrainian conflict.
  • D. Kherson
    Kherson is a port city in southern Ukraine near the Black Sea, historically significant as a shipbuilding and industrial center and strategically important due to its location on the Dnieper River.
  • E. Dnipro
    Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca8484838c8190b2049199d22fef70 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a3a49608190ad1f65195e4d5cda completed April 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69d7faf8c1048190a136289a44a0930b completed April 9, 2026, 7:16 p.m.
Created at: March 30, 2026, 8:07 p.m.